小语言模型用逻辑语言可高效完成推理，助力知识图谱构建
Investigating Language Model Capabilities to Represent and Process Formal Knowledge: A Preliminary Study to Assist Ontology Engineering
- 用紧凑的逻辑语言替代自然语言输入，提升推理效率
- 在预设推理任务中保持高准确率，验证逻辑表达有效性
- 适合从事知识工程、自动建模的研究者参考
近期语言模型在推理能力方面仍存在明显短板，尤其影响知识图谱工程等任务。作为博士研究的一部分，我们探究了引入形式化方法对小型语言模型（SLMs）在推理任务中表现的影响。重点探索利用SLMs辅助知识图谱构建的可行性，并设计一系列初步实验，考察以不同语法表达逻辑问题对SLMs在指定推理任务上性能的影响。结果表明，用更紧凑的逻辑语言替代自然语言，可在保持强推理性能的同时显著提升表达效率，为后续优化SLMs在知识工程中的角色提供了依据。
原文摘要 · Abstract (English)
Recent advances in Language Models (LMs) have failed to mask their shortcomings particularly in the domain of reasoning. This limitation impacts several tasks, most notably those involving ontology engineering. As part of a PhD research, we investigate the consequences of incorporating formal methods on the performance of Small Language Models (SLMs) on reasoning tasks. Specifically, we aim to orient our work toward using SLMs to bootstrap ontology construction and set up a series of preliminary experiments to determine the impact of expressing logical problems with different grammars on the performance of SLMs on a predefined reasoning task. Our findings show that it is possible to substitute Natural Language (NL) with a more compact logical language while maintaining a strong performance on reasoning tasks and hope to use these results to further refine the role of SLMs in ontology engineering.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。